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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Ho Nieh nominated to the NRC
Nieh
President Trump recently nominated Ho Nieh for the role of commissioner in the Nuclear Regulatory Commission through the remainder of a term that will expire June 30, 2029.
Nieh has been the vice president of regulatory affairs at Southern Nuclear since 2021, though he is currently working as a loaned executive at the Institute of Nuclear Power Operations, where he has been for more than a year.
Nieh’s experience: Nieh started his career at the Knolls Atomic Power Laboratory, where he worked primarily as a nuclear plant engineer and contributed as a civilian instructor in the U.S. Navy’s Nuclear Power Program.
From there, he joined the NRC in 1997 as a project engineer. In more than 19 years of service at the organization, he served in a variety of key leadership roles, including division director of Reactor Projects, division director of Inspection and Regional Support, and director of the Office of Nuclear Reactor Regulation.
Chen Dubi, Anil K. Prinja
Nuclear Science and Engineering | Volume 199 | Number 1 | April 2025 | Pages S649-S663
Research Article | doi.org/10.1080/00295639.2024.2324532
Articles are hosted by Taylor and Francis Online.
Modeling and simulation of stochastic fission chains, often referred to as zero power reactor noise or stochastic transport, is a central topic in nuclear science and engineering, with important practical applications in reactor control and measurements. The common mathematical setting for studying reactor noise is through branching processes, typically modeled using forward and backward master equations. Because of the high complexity of the problem, reactor noise is typically studied under the point model approximation and in a linear setting, which neglects any reactivity feedbacks. On the other hand, since reactivity feedbacks are dominant in power reactors, stochastic models and simulations in a nonlinear setting are of great interest. For this reason, there is a constant interest in new mathematical frameworks that will enable modeling and simulation in a complete fashion. In the present study, we look at a branching process with a negative feedback, realized by a linear increase in the per capita death rate. In particular, the outline of the study is to compare two models for the above process. The first is the exact model, based on a forward master equation, and the second is the diffusion scale approximation (or the functional central limit approximation), realized in an Ito-type stochastic differential equation (SDE). The comparison between the two models is done in two levels. First, we show that the dynamics of the first and second moments of both models are governed by the same equations. Then, by realization of each model, we contract a Monte Carlo simulation for sampling the population size distribution. In particular, we show that once the population size is sufficiently large, the SDE approximation allows us to perform accurate simulations in a critical setting, with a dramatic reduction of the CPU time.